Bibliographic record
Abstract
Lidar (Light detection and ranging) is a relatively new method for the acquisition of terrain surface. The Lidar system not only generates the 3-dimensional cloud of points with irregular spacing, but also gets the laser impulse reflection data. Algorithms and software used for the Lidar data are deal with the 3-dimensional cloud of points, and get the DTM (Digital Terrain Model). The algorithms apply in the laser impulse reflection data are little. According to the character of Lidar data, which is fused the 3-dimension information and intensity information in pixel-level, two new fusion filtering algorithms are proposed in this paper. These algorithms translate the intensity information of Lidar data into intensity image, and fused the 3- dimension information, mend the traditional image procession ways to deal with the Lidar data. All the algorithms mentioned are applied in Lidar data, and their results are compared in different evaluation parameters. The conclusion proved the improvement of these proposed algorithms in keeping the advantage of traditional filtering algorithms under the condition of preserving edges information of the intensity image.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".